options(repos = "http://cran.us.r-project.org")
# Install and load required packages
required_packages <- c("ggpubr", "tidyverse", "ggplot2", "plotly", "moments", "DT", "LambertW")
for (package in required_packages) {
if (!require(package, character.only = TRUE)) {
install.packages(package)
library(package, character.only = TRUE)
}
}gapminder_filtered <- filter(gapminder, Year == 1962)
gapminder_plot <- ggplot(gapminder_filtered, aes(x = log(gdpPercap),
y = log(`CO2 emissions (metric tons per capita)`))) +
labs(x = 'Log base 10 transform of gdpPercap', y = 'Log base 10 transform of CO2 emissions (metric tons per capita)')+
geom_point()+
theme(text = element_text(size = 10))
gapminder_plotcorrelation <- cor(gapminder_filtered$`CO2 emissions (metric tons per capita)`,
gapminder_filtered$gdpPercap, use = "complete.obs")
p_value <- cor.test(gapminder_filtered$`CO2 emissions (metric tons per capita)`,
gapminder_filtered$gdpPercap)$p.value
print(paste("The correlation coefficient is", as.character(correlation)))## [1] "The correlation coefficient is 0.926081672501945"
## [1] "The p-value is 1.1286792210055e-46"
We want to test if there is a statistically significant difference in the Energy use per continent. Thus, continent is the predictor variable, and energy use is the outcome variable.
To use a parametric test, we must ensure that three assumptions are met: Normality, equal variances, and independence.
We visually assess normality using histograms of energy use for each continent
ggplot(gapminder, aes(x = `Energy use (kg of oil equivalent per capita)`)) +
geom_histogram(bins = 30) +
facet_wrap(~ continent, scales = "free") +
xlab("Energy use (kg of oil equivalent per capita)") +
ylab("Frequency")Visually, the data is not normally distributed. Therefore, we use the non-parametric Kruskal-Wallis test
kruskal.test(gapminder$`Energy use (kg of oil equivalent per capita)`, gapminder$continent, na.action = "na.omit")##
## Kruskal-Wallis rank sum test
##
## data: gapminder$`Energy use (kg of oil equivalent per capita)` and gapminder$continent
## Kruskal-Wallis chi-squared = 318.68, df = 4, p-value < 2.2e-16
As we can see, the p value is less than 2.2e-16, which is less than 0.05, which means that the energy use varies significantly between at least two continents.
# Create box plots
box_plot <- ggplot(gapminder_years, aes(x = continent, y = `Imports of goods and services (% of GDP)`, fill = continent)) +
geom_boxplot() +
labs(x = "Continent", y = "Imports of goods and services (% of GDP)", fill = "Continent") +
ggtitle("Box Plot of GDP Imports by Continent")
ggplotly(box_plot)# Create density plots
density_plot <- ggplot(gapminder_years, aes(x = `Imports of goods and services (% of GDP)`, fill = continent)) +
geom_density(alpha = 0.5) +
labs(x = "Imports of goods and services (% of GDP)", fill = "Continent") +
ggtitle("Density Plot of GDP Imports by Continent")
ggplotly(density_plot)Visually, the two continent’s import of goods and services are very close with overlapping peaks, although the variances appear to be different. There appears to be 4 outliers in Asia.
# Filter data for the years after 1990
data <- gapminder_years
# Plot Q-Q plot with facet by continent
ggplotly(ggqqplot(data = gapminder_years, x = "`Imports of goods and services (% of GDP)`", facet.by = "continent"))For Asia, there are a few points with a high GDP above the diagonal line. As normality has been violated, it would not be appropriate to use a parametric test, so we use the non-parametric Mann-Whitney-Wilcoxon Test.
result <- wilcox.test(`Imports of goods and services (% of GDP)` ~ continent, data = gapminder_years)
print(result)##
## Wilcoxon rank sum test with continuity correction
##
## data: Imports of goods and services (% of GDP) by continent
## W = 5707, p-value = 0.7867
## alternative hypothesis: true location shift is not equal to 0
As the p value of 0.7867 is greater than 0.05, we did not find a significant difference in ‘Imports of goods and services (% of GDP)’ between Europe and Asia.
'Population density (people per sq. km of land area)'
across all years? (i.e., which country has the highest average ranking
in this category across each time point in the dataset?)# Getting the ranked populations descending (the country with the greatest population density would be #1)
gapminder_pd <- gapminder %>%
group_by(`Year`) %>%
mutate(population_rank = rank(-`Population density (people per sq. km of land area)`)) %>%
dplyr::select(population_rank, `Country Name`)## Adding missing grouping variables: `Year`
# Taking the average population rank
gapminder_rank_mean <- gapminder_pd %>%
group_by(`Country Name`) %>%
summarize(mean_population_density_rank = mean(population_rank)) %>%
arrange(mean_population_density_rank) %>%
slice(1:5)
gapminder_rank_mean_plot <- ggplot(data = gapminder_rank_mean, aes(x = `Country Name`, y = mean_population_density_rank)) +
geom_bar(stat = 'Identity') +
labs(title = "Countries with greatest average ranking for population density", y = 'Mean rank of country')
gapminder_rank_mean_plotAs seen from bar chart, Macao SAR, China and Monaco are tied for
having the highest
'Population density (people per sq. km of land area)'
across all years; each had an average rank of 1.5.
'Life expectancy at birth, total (years)' between 1962 and
2007?# Get the top 5 countries with the greatest increase in life expectancies
gapminder_difference <-gapminder %>%
filter(Year %in% c(1962, 2007)) %>%
group_by(`Country Name`)%>%
arrange((`Life expectancy at birth, total (years)`)) %>%
reframe(`Difference in Life expectancy (2007 - 1962)` = diff(`Life expectancy at birth, total (years)`)) %>%
arrange(desc(`Difference in Life expectancy (2007 - 1962)`)) %>%
slice(1:5)
# Plotting the top 5 countries
gapminder_difference_plot <- gapminder_difference %>%
ggplot(aes(x = `Country Name`, y = `Difference in Life expectancy (2007 - 1962)`)) +
geom_bar(stat = "identity") +
labs(title = "Top 5 countries with the greatest increase in life expectancy from 1962 to 2007", y = 'Difference in Life expectancy in years for (2007 - 1962)')
ggplotly(gapminder_difference_plot)As seen from the above bar chart, the country whose life expectancy increased the most from 1962 - 2007 is Maldives